code: def testKeypointHead(self):
      class M(torch.nn.Module):
          def __init__(self):
              super().__init__()
              self.model = KRCNNConvDeconvUpsampleHead(
                  ShapeSpec(channels=4, height=14, width=14),
                  num_keypoints=17,
                  conv_dims=(4,),
              )
  
          def forward(self, x, predbox1, predbox2):
              inst = [
                  Instances((100, 100), pred_boxes=Boxes(predbox1)),
                  Instances((100, 100), pred_boxes=Boxes(predbox2)),
              ]
              ret = self.model(x, inst)
              return tuple(x.pred_keypoints for x in ret)
  
      model = M()
      model.eval()
  
      def gen_input(num1, num2):
          feat = torch.randn((num1 + num2, 4, 14, 14))
          box1 = random_boxes(num1)
          box2 = random_boxes(num2)
          return feat, box1, box2
  
      with patch_builtin_len():
          onnx_model = self._test_model(
              model,
              gen_input(1, 2),
              input_names=["features", "pred_boxes", "pred_classes"],
              output_names=["box1", "box2"],
              dynamic_axes={
                  "features": {
                      0: "batch",
                      1: "static_four",
                      2: "height",
                      3: "width",
                  },
                  "pred_boxes": {0: "batch", 1: "static_four"},
                  "pred_classes": {0: "batch", 1: "static_four"},
                  "box1": {
                      0: "num_instance",
                      1: "K",
                      2: "static_three",
                  },
                  "box2": {
                      0: "num_instance",
                      1: "K",
                      2: "static_three",
                  },
              },
          )
  
          # Although ONNX models are not executable by PyTorch to verify
          # support of batches with different sizes, we can verify model's IR
          # does not hard-code input and/or output shapes.
          # TODO: Add tests with different batch sizes when detectron2's CI
          #       support ONNX Runtime backend.
          assert has_dynamic_axes(onnx_model)
  
imports: import io
  import unittest
  import warnings
  import torch
  from torch.hub import _check_module_exists
  from detectron2 import model_zoo
  from detectron2.config import get_cfg
  from detectron2.export import STABLE_ONNX_OPSET_VERSION
  from detectron2.export.flatten import TracingAdapter
  from detectron2.export.torchscript_patch import patch_builtin_len
  from detectron2.layers import ShapeSpec
  from detectron2.modeling import build_model
  from detectron2.modeling.roi_heads import KRCNNConvDeconvUpsampleHead
  from detectron2.structures import Boxes, Instances
  from detectron2.utils.testing import _pytorch1111_symbolic_opset9_repeat_interleave, _pytorch1111_symbolic_opset9_to, get_sample_coco_image, has_dynamic_axes, random_boxes, register_custom_op_onnx_export, skipIfOnCPUCI, skipIfUnsupportedMinOpsetVersion, skipIfUnsupportedMinTorchVersion, unregister_custom_op_onnx_export
label_desc: Use image node_id 7 for calling the TestONNXTracingExport obj's underlying member method code with example usage: obj.testKeypointHead() and returns: feat,  box1,  box2, tuple